Learning a bayesian network from ordinal data
AbstractBayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data. In this paper, we propose a constraint-based method to perform Bayesian networks structural learning in presence of ordinal variables. The new procedure, called OPC, represents a variation of the PC algorithm. A nonparametric test, appropriate for ordinal variables, has been used. It will be shown that, in some situation, the OPC algorithm is a solution more efficient than the PC algorithm.
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Bibliographic InfoPaper provided by Department of Economics - University Roma Tre in its series Departmental Working Papers of Economics - University 'Roma Tre' with number 0139.
Date of creation: Oct 2011
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Structural Learning; Monotone Association; Nonparametric Methods;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-12-13 (All new papers)
- NEP-ECM-2011-12-13 (Econometrics)
- NEP-NET-2011-12-13 (Network Economics)
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